






"""simple docstring"""





import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

_SCREAMING_SNAKE_CASE   :							Tuple          =		[
    '''word_embeddings_layernorm.weight''',
    '''word_embeddings_layernorm.bias''',
    '''input_layernorm.weight''',
    '''input_layernorm.bias''',
    '''post_attention_layernorm.weight''',
    '''post_attention_layernorm.bias''',
    '''self_attention.dense.bias''',
    '''mlp.dense_4h_to_h.bias''',
    '''ln_f.weight''',
    '''ln_f.bias''',
]

_SCREAMING_SNAKE_CASE   :							Optional[int]          =		[
    '''mlp.dense_4h_to_h.weight''',
    '''self_attention.dense.weight''',
]







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						List[Any]					) ->							List[Any]:
  lowerCamelCase_       	=		{
      'word_embeddings.weight': 'word_embeddings.weight',
      'word_embeddings.norm.weight': 'word_embeddings_layernorm.weight',
      'word_embeddings.norm.bias': 'word_embeddings_layernorm.bias',
      'weight': 'ln_f.weight',
      'bias': 'ln_f.bias',
  }

  if key in layer_rename_map:
    return layer_rename_map[key]

  # Handle transformer blocks
  lowerCamelCase_       	=		int(re.match(r'.*layer_(\d*).*'						,							_lowerCamelCase					)[1]					)
  layer_number -= 3
  return F'''h.{layer_number}.''' + key







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[int]					) ->							Dict:
  if dtype == torch.bool:
    return 1 / 8
  lowerCamelCase_       	=		re.search(r'[^\d](\d+)$'						,							str(_lowerCamelCase					)					)
  if bit_search is None:
    raise ValueError(F'''`dtype` is not a valid dtype: {dtype}.'''					)
  lowerCamelCase_       	=		int(bit_search.groups()[0]					)
  return bit_size // 8







def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Tuple					) ->							Optional[int]:
  # Construct model
  if bloom_config_file == "":
    lowerCamelCase_       	=		BloomConfig()
  else:
    lowerCamelCase_       	=		BloomConfig.from_json_file(_lowerCamelCase					)

  if shard_model:
    lowerCamelCase_       	=		os.listdir(_lowerCamelCase					)
    lowerCamelCase_       	=		sorted(filter(lambda _lowerCamelCase					: s.startswith('layer'					) and "model_00" in s						,							_lowerCamelCase					)					)

    lowerCamelCase_       	=		{'weight_map': {}, 'metadata': {}}
    lowerCamelCase_       	=		0

    lowerCamelCase_       	=		None

    lowerCamelCase_       	=		BloomConfig()

    for j, file in enumerate(_lowerCamelCase					):
      print('Processing file: {}'.format(_lowerCamelCase					)					)
      lowerCamelCase_       	=		None

      for i in range(_lowerCamelCase					):
        # load all TP files
        lowerCamelCase_       	=		file.replace('model_00'						,							F'''model_0{i}'''					)
        lowerCamelCase_       	=		torch.load(os.path.join(_lowerCamelCase						,							_lowerCamelCase					)						,							map_location='cpu'					)

        # Rename keys in the transformers names
        lowerCamelCase_       	=		list(temp.keys()					)
        for key in keys:
          lowerCamelCase_       	=		temp.pop(_lowerCamelCase					)

        if tensors is None:
          lowerCamelCase_       	=		temp
        else:
          for key in tensors.keys():
            if any(key.endswith(_lowerCamelCase					) for end in WEIGHTS_TO_AVERAGE_ENDSWITH					):
              # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
              tensors[key] += temp[key]
            else:
              # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
              lowerCamelCase_       	=		1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN					) else 0
              # We concatenate these weights accross TP ranks
              lowerCamelCase_       	=		torch.cat([tensors[key], temp[key]]						,							dim=_lowerCamelCase					)

            # Divide by the number of TP the weights we want to average
      for key in tensors.keys():
        if any(key.endswith(_lowerCamelCase					) for end in WEIGHTS_TO_AVERAGE_ENDSWITH					):
          lowerCamelCase_       	=		tensors[key] / pretraining_tp
      torch.save(
          _lowerCamelCase						,							os.path.join(
              _lowerCamelCase						,							'pytorch_model_{}-of-{}.bin'.format(str(j + 1					).zfill(5					)						,							str(len(_lowerCamelCase					)					).zfill(5					)					)						,							)						,							)

      for key in tensors.keys():
        lowerCamelCase_       	=		tensors[key]
        total_size += value.numel() * get_dtype_size(value.dtype					)
        if key not in index_dict["weight_map"]:
          lowerCamelCase_       	=		'pytorch_model_{}-of-{}.bin'.format(
              str(j + 1					).zfill(5					)						,							str(len(_lowerCamelCase					)					).zfill(5					)					)

    lowerCamelCase_       	=		BloomConfig()
    lowerCamelCase_       	=		pytorch_dump_folder_path + '/' + CONFIG_NAME
    lowerCamelCase_       	=		total_size
    with open(_lowerCamelCase						,							'w'						,							encoding='utf-8'					) as f:
      f.write(config.to_json_string()					)
    with open(os.path.join(_lowerCamelCase						,							WEIGHTS_NAME + '.index.json'					)						,							'w'						,							encoding='utf-8'					) as f:
      lowerCamelCase_       	=		json.dumps(_lowerCamelCase						,							indent=2						,							sort_keys=_lowerCamelCase					) + '\n'
      f.write(_lowerCamelCase					)
  else:
    lowerCamelCase_       	=		BloomModel(_lowerCamelCase					)

    lowerCamelCase_       	=		os.listdir(_lowerCamelCase					)
    lowerCamelCase_       	=		sorted(filter(lambda _lowerCamelCase					: s.startswith('layer'					) and "model_00" in s						,							_lowerCamelCase					)					)

    lowerCamelCase_       	=		None
    for i, file in enumerate(_lowerCamelCase					):
      lowerCamelCase_       	=		None
      for i in range(_lowerCamelCase					):
        # load all TP files
        lowerCamelCase_       	=		file.replace('model_00'						,							F'''model_0{i}'''					)
        lowerCamelCase_       	=		torch.load(os.path.join(_lowerCamelCase						,							_lowerCamelCase					)						,							map_location='cpu'					)

        # Rename keys in the transformers names
        lowerCamelCase_       	=		list(temp.keys()					)
        for key in keys:
          lowerCamelCase_       	=		temp.pop(_lowerCamelCase					)

        if tensors is None:
          lowerCamelCase_       	=		temp
        else:
          for key in tensors.keys():
            # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
            if any(key.endswith(_lowerCamelCase					) for end in WEIGHTS_TO_AVERAGE_ENDSWITH					):
              tensors[key] += temp[key]
            else:
              # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
              lowerCamelCase_       	=		1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN					) else 0
              # We concatenate these weights accross TP ranks
              lowerCamelCase_       	=		torch.cat([tensors[key], temp[key]]						,							dim=_lowerCamelCase					)

            # Divide by the number of TP the weights we want to average
      for key in tensors.keys():
        if any(key.endswith(_lowerCamelCase					) for end in WEIGHTS_TO_AVERAGE_ENDSWITH					):
          lowerCamelCase_       	=		tensors[key] / pretraining_tp

      lowerCamelCase_       	=		model.load_state_dict(_lowerCamelCase						,							strict=_lowerCamelCase					)
      assert not other_keys.unexpected_keys, F'''The keys {other_keys.unexpected_keys} are unexpected'''
      if missing_keys is None:
        lowerCamelCase_       	=		set(other_keys.missing_keys					)
      else:
        lowerCamelCase_       	=		missing_keys.intersection(set(other_keys.missing_keys					)					)

    assert not missing_keys, F'''The keys {missing_keys} are missing'''

    # Save pytorch-model
    os.makedirs(_lowerCamelCase						,							exist_ok=_lowerCamelCase					)
    lowerCamelCase_       	=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
    lowerCamelCase_       	=		pytorch_dump_folder_path + '/' + CONFIG_NAME
    print(F'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}'''					)
    if config.torch_dtype is not None:
      lowerCamelCase_       	=		model.to(config.torch_dtype					)
    torch.save(model.state_dict()						,							_lowerCamelCase					)
    print(F'''Save configuration file to {pytorch_config_dump_path}'''					)
    with open(_lowerCamelCase						,							'w'						,							encoding='utf-8'					) as f:
      f.write(config.to_json_string()					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							Optional[int]          =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--bloom_checkpoint_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to the Megatron-LM checkpoint path.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--bloom_config_file''',
      default='''''',
      type=str,
      help=(
          '''An optional config json file corresponding to the pre-trained model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--shard_model''',
      action='''store_true''',
      help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
  )
  parser.add_argument(
      '''--pretraining_tp''',
      default=4,
      type=int,
      help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
  )
  _SCREAMING_SNAKE_CASE   :							Optional[int]          =		parser.parse_args()
  convert_bloom_checkpoint_to_pytorch(
      args.bloom_checkpoint_path,
      args.bloom_config_file,
      args.pytorch_dump_folder_path,
      args.shard_model,
      args.pretraining_tp,
  )












"""simple docstring"""





import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_SCREAMING_SNAKE_CASE   :							str          =		logging.getLogger(__name__)






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					"""summarization"""
    SCREAMING_SNAKE_CASE		:	Any		=					["""loss"""]
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					ROUGE_KEYS
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					"""rouge2"""






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		**__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Union[str, Any]:
      if hparams.sortish_sampler and hparams.gpus > 1:
        lowerCamelCase_       	=		False
      elif hparams.max_tokens_per_batch is not None:
        if hparams.gpus > 1:
          raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'   )
        if hparams.sortish_sampler:
          raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'   )

      super().__init__(__SCREAMING_SNAKE_CASE			,		num_labels=__SCREAMING_SNAKE_CASE			,		mode=self.mode			,		**__SCREAMING_SNAKE_CASE   )
      use_task_specific_params(self.model			,		'summarization'   )
      save_git_info(self.hparams.output_dir   )
      lowerCamelCase_       	=		Path(self.output_dir   ) / 'metrics.json'
      lowerCamelCase_       	=		Path(self.output_dir   ) / 'hparams.pkl'
      pickle_save(self.hparams			,		self.hparams_save_path   )
      lowerCamelCase_       	=		0
      lowerCamelCase_       	=		defaultdict(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.config.model_type
      lowerCamelCase_       	=		self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

      lowerCamelCase_       	=		{
          "data_dir": self.hparams.data_dir,
          "max_source_length": self.hparams.max_source_length,
          "prefix": self.model.config.prefix or "",
      }
      lowerCamelCase_       	=		{
          'train': self.hparams.n_train,
          'val': self.hparams.n_val,
          'test': self.hparams.n_test,
      }
      lowerCamelCase_       	=		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

      lowerCamelCase_       	=		{
          'train': self.hparams.max_target_length,
          'val': self.hparams.val_max_target_length,
          'test': self.hparams.test_max_target_length,
      }
      assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
      assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
      if self.hparams.freeze_embeds:
        freeze_embeds(self.model   )
      if self.hparams.freeze_encoder:
        freeze_params(self.model.get_encoder()   )
        assert_all_frozen(self.model.get_encoder()   )

      lowerCamelCase_       	=		get_git_info()['repo_sha']
      lowerCamelCase_       	=		hparams.num_workers
      lowerCamelCase_       	=		None  # default to config
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
        lowerCamelCase_       	=		self.decoder_start_token_id
      lowerCamelCase_       	=		(
          SeqaSeqDataset if hasattr(self.tokenizer			,		'prepare_seq2seq_batch'   ) else LegacySeqaSeqDataset
      )
      lowerCamelCase_       	=		False
      lowerCamelCase_       	=		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
      if self.hparams.eval_max_gen_length is not None:
        lowerCamelCase_       	=		self.hparams.eval_max_gen_length
      else:
        lowerCamelCase_       	=		self.model.config.max_length
      lowerCamelCase_       	=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Dict[str, torch.Tensor]   )	->						Dict[str, List[str]]:
      lowerCamelCase_       	=		{
          k: self.tokenizer.batch_decode(v.tolist()   ) if 'mask' not in k else v.shape for k, v in batch.items()
      }
      save_json(__SCREAMING_SNAKE_CASE			,		Path(self.output_dir   ) / 'text_batch.json'   )
      save_json({k: v.tolist() for k, v in batch.items()}			,		Path(self.output_dir   ) / 'tok_batch.json'   )

      lowerCamelCase_       	=		True
      return readable_batch






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		**__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						Any:
      return self.model(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[int]   )	->						int:
      lowerCamelCase_       	=		self.tokenizer.batch_decode(
          __SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE			,		clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
      return lmap(str.strip			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				dict   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer.pad_token_id
      lowerCamelCase_	,      lowerCamelCase_       	=		batch['input_ids'], batch['attention_mask']
      lowerCamelCase_       	=		batch['labels']
      if isinstance(self.model			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		self.model._shift_right(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		shift_tokens_right(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
        lowerCamelCase_       	=		decoder_input_ids
        self.save_readable_batch(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self(__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE			,		decoder_input_ids=__SCREAMING_SNAKE_CASE			,		use_cache=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		outputs['logits']
      if self.hparams.label_smoothing == 0:
        # Same behavior as modeling_bart.py, besides ignoring pad_token_id
        lowerCamelCase_       	=		nn.CrossEntropyLoss(ignore_index=__SCREAMING_SNAKE_CASE   )

        assert lm_logits.shape[-1] == self.vocab_size
        lowerCamelCase_       	=		ce_loss_fct(lm_logits.view(-1			,		lm_logits.shape[-1]   )			,		tgt_ids.view(-1   )   )
      else:
        lowerCamelCase_       	=		nn.functional.log_softmax(__SCREAMING_SNAKE_CASE			,		dim=-1   )
        lowerCamelCase_	,      lowerCamelCase_       	=		label_smoothed_nll_loss(
            __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		self.hparams.label_smoothing			,		ignore_index=__SCREAMING_SNAKE_CASE   )
      return (loss,)






    @property
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						int:
      return self.tokenizer.pad_token_id






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		self._step(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		dict(zip(self.loss_names			,		__SCREAMING_SNAKE_CASE   )   )
      # tokens per batch
      lowerCamelCase_       	=		batch['input_ids'].ne(self.pad   ).sum() + batch['labels'].ne(self.pad   ).sum()
      lowerCamelCase_       	=		batch['input_ids'].shape[0]
      lowerCamelCase_       	=		batch['input_ids'].eq(self.pad   ).sum()
      lowerCamelCase_       	=		batch['input_ids'].eq(self.pad   ).float().mean()
      # TODO(SS): make a wandb summary metric for this
      return {"loss": loss_tensors[0], "log": logs}






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Dict:
      return self._generative_step(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]="val"   )	->						Dict:
      self.step_count += 1
      lowerCamelCase_       	=		{k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
      lowerCamelCase_       	=		losses['loss']
      lowerCamelCase_       	=		{
          k: np.array([x[k] for x in outputs]   ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
      }
      lowerCamelCase_       	=		(
          generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
      )
      lowerCamelCase_       	=		torch.tensor(__SCREAMING_SNAKE_CASE   ).type_as(__SCREAMING_SNAKE_CASE   )
      generative_metrics.update({k: v.item() for k, v in losses.items()}   )
      losses.update(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
      lowerCamelCase_       	=		self.step_count
      self.metrics[prefix].append(__SCREAMING_SNAKE_CASE   )  # callback writes this to self.metrics_save_path
      lowerCamelCase_       	=		flatten_list([x['preds'] for x in outputs]   )
      return {
          "log": all_metrics,
          "preds": preds,
          F'''{prefix}_loss''': loss,
          F'''{prefix}_{self.val_metric}''': metric_tensor,
      }






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Dict:
      return calculate_rouge(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				dict   )	->						dict:
      lowerCamelCase_       	=		time.time()

      # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
      lowerCamelCase_       	=		self.model.generate(
          batch['input_ids']			,		attention_mask=batch['attention_mask']			,		use_cache=__SCREAMING_SNAKE_CASE			,		decoder_start_token_id=self.decoder_start_token_id			,		num_beams=self.eval_beams			,		max_length=self.eval_max_length			,		)
      lowerCamelCase_       	=		(time.time() - ta) / batch['input_ids'].shape[0]
      lowerCamelCase_       	=		self.ids_to_clean_text(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.ids_to_clean_text(batch['labels']   )
      lowerCamelCase_       	=		self._step(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		dict(zip(self.loss_names			,		__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		self.calc_generative_metrics(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		np.mean(lmap(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )
      base_metrics.update(gen_time=__SCREAMING_SNAKE_CASE			,		gen_len=__SCREAMING_SNAKE_CASE			,		preds=__SCREAMING_SNAKE_CASE			,		target=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
      return base_metrics






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Any:
      return self._generative_step(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Tuple:
      return self.validation_epoch_end(__SCREAMING_SNAKE_CASE			,		prefix='test'   )






    def   UpperCamelCase			(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						SeqaSeqDataset:
      lowerCamelCase_       	=		self.n_obs[type_path]
      lowerCamelCase_       	=		self.target_lens[type_path]
      lowerCamelCase_       	=		self.dataset_class(
          self.tokenizer			,		type_path=__SCREAMING_SNAKE_CASE			,		n_obs=__SCREAMING_SNAKE_CASE			,		max_target_length=__SCREAMING_SNAKE_CASE			,		**self.dataset_kwargs			,		)
      return dataset






    def   UpperCamelCase			(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				bool = False   )	->						DataLoader:
      lowerCamelCase_       	=		self.get_dataset(__SCREAMING_SNAKE_CASE   )

      if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
        lowerCamelCase_       	=		dataset.make_sortish_sampler(__SCREAMING_SNAKE_CASE			,		distributed=self.hparams.gpus > 1   )
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_size=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		shuffle=__SCREAMING_SNAKE_CASE			,		num_workers=self.num_workers			,		sampler=__SCREAMING_SNAKE_CASE			,		)

      elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
        lowerCamelCase_       	=		dataset.make_dynamic_sampler(
            self.hparams.max_tokens_per_batch			,		distributed=self.hparams.gpus > 1   )
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_sampler=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		num_workers=self.num_workers			,		)
      else:
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_size=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		shuffle=__SCREAMING_SNAKE_CASE			,		num_workers=self.num_workers			,		sampler=__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Dict   )	->						DataLoader:
      lowerCamelCase_       	=		self.get_dataloader('train'			,		batch_size=self.hparams.train_batch_size			,		shuffle=__SCREAMING_SNAKE_CASE   )
      return dataloader






    def   UpperCamelCase			(			self	:				int   )	->						DataLoader:
      return self.get_dataloader('val'			,		batch_size=self.hparams.eval_batch_size   )






    def   UpperCamelCase			(			self	:				int   )	->						DataLoader:
      return self.get_dataloader('test'			,		batch_size=self.hparams.eval_batch_size   )






    @staticmethod
    def   UpperCamelCase			(			__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Dict:
      BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      add_generic_args(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      parser.add_argument(
          '--max_source_length'			,		default=1024			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--max_target_length'			,		default=56			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--val_max_target_length'			,		default=142			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--test_max_target_length'			,		default=142			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument('--freeze_encoder'			,		action='store_true'   )
      parser.add_argument('--freeze_embeds'			,		action='store_true'   )
      parser.add_argument('--sortish_sampler'			,		action='store_true'			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--overwrite_output_dir'			,		action='store_true'			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--max_tokens_per_batch'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--logger_name'			,		type=__SCREAMING_SNAKE_CASE			,		choices=['default', 'wandb', 'wandb_shared']			,		default='default'   )
      parser.add_argument('--n_train'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--n_val'			,		type=__SCREAMING_SNAKE_CASE			,		default=500			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--n_test'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument(
          '--task'			,		type=__SCREAMING_SNAKE_CASE			,		default='summarization'			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--label_smoothing'			,		type=__SCREAMING_SNAKE_CASE			,		default=0.0			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--src_lang'			,		type=__SCREAMING_SNAKE_CASE			,		default=''			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--tgt_lang'			,		type=__SCREAMING_SNAKE_CASE			,		default=''			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--eval_beams'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument(
          '--val_metric'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		required=__SCREAMING_SNAKE_CASE			,		choices=['bleu', 'rouge2', 'loss', None]   )
      parser.add_argument('--eval_max_gen_length'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		help='never generate more than n tokens'   )
      parser.add_argument('--save_top_k'			,		type=__SCREAMING_SNAKE_CASE			,		default=1			,		required=__SCREAMING_SNAKE_CASE			,		help='How many checkpoints to save'   )
      parser.add_argument(
          '--early_stopping_patience'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help=(
              '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
              ' val_check_interval will effect it.'
          )			,		)
      return parser






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					"""translation"""
    SCREAMING_SNAKE_CASE		:	List[str]		=					["""loss"""]
    SCREAMING_SNAKE_CASE		:	str		=					["""bleu"""]
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					"""bleu"""






    def __init__(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Dict			,		**__SCREAMING_SNAKE_CASE	:				Tuple   )	->						Optional[Any]:
      super().__init__(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		hparams.src_lang
      lowerCamelCase_       	=		hparams.tgt_lang






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						dict:
      return calculate_bleu(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Tuple=None					) ->							SummarizationModule:
  Path(args.output_dir					).mkdir(exist_ok=_lowerCamelCase					)
  check_output_dir(_lowerCamelCase						,							expected_items=3					)

  if model is None:
    if "summarization" in args.task:
      lowerCamelCase_       	=		SummarizationModule(_lowerCamelCase					)
    else:
      lowerCamelCase_       	=		TranslationModule(_lowerCamelCase					)
  lowerCamelCase_       	=		Path(args.data_dir					).name
  if (
      args.logger_name == "default"
      or args.fast_dev_run
      or str(args.output_dir					).startswith('/tmp'					)
      or str(args.output_dir					).startswith('/var'					)
  ):
    lowerCamelCase_       	=		True  # don't pollute wandb logs unnecessarily
  elif args.logger_name == "wandb":
    from pytorch_lightning.loggers import WandbLogger

    lowerCamelCase_       	=		os.environ.get('WANDB_PROJECT'						,							_lowerCamelCase					)
    lowerCamelCase_       	=		WandbLogger(name=model.output_dir.name						,							project=_lowerCamelCase					)

  elif args.logger_name == "wandb_shared":
    from pytorch_lightning.loggers import WandbLogger

    lowerCamelCase_       	=		WandbLogger(name=model.output_dir.name						,							project=F'''hf_{dataset}'''					)

  if args.early_stopping_patience >= 0:
    lowerCamelCase_       	=		get_early_stopping_callback(model.val_metric						,							args.early_stopping_patience					)
  else:
    lowerCamelCase_       	=		False

  lowerCamelCase_       	=		args.val_metric == 'loss'
  lowerCamelCase_       	=		generic_train(
      _lowerCamelCase						,							_lowerCamelCase						,							logging_callback=SeqaSeqLoggingCallback()						,							checkpoint_callback=get_checkpoint_callback(
          args.output_dir						,							model.val_metric						,							args.save_top_k						,							_lowerCamelCase					)						,							early_stopping_callback=_lowerCamelCase						,							logger=_lowerCamelCase						,							)
  pickle_save(model.hparams						,							model.output_dir / 'hparams.pkl'					)
  if not args.do_predict:
    return model

  lowerCamelCase_       	=		''
  lowerCamelCase_       	=		sorted(glob.glob(os.path.join(args.output_dir						,							'*.ckpt'					)						,							recursive=_lowerCamelCase					)					)
  if checkpoints:
    lowerCamelCase_       	=		checkpoints[-1]
    lowerCamelCase_       	=		checkpoints[-1]
  trainer.logger.log_hyperparams(model.hparams					)

  # test() without a model tests using the best checkpoint automatically
  trainer.test()
  return model


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							List[str]          =		argparse.ArgumentParser()
  _SCREAMING_SNAKE_CASE   :							Any          =		pl.Trainer.add_argparse_args(parser)
  _SCREAMING_SNAKE_CASE   :							Any          =		SummarizationModule.add_model_specific_args(parser, os.getcwd())

  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		parser.parse_args()

  main(args)




